Structured risk assessment tools estimate risk to reoffend.
Based on statistical analysis of theoretically based factors about a youth’s social situation and delinquency history.
Research shows outcomes are maximized.
Adopted by all states in U.S. and numerous countries throughout the world.
Wide range of costs of services as low as $11 (Probation) to $1,224 (Adult Living for Transitional Achievement) per youth per day.
Policies shaped by risk assessment data.
High level of self-reported trauma, abuse, and mental health.
Society and system tend to have a different view (protect & punish).
Challenges from male dominated society.
On average, come to the system via different pathways than boys.
Research is male focused.
Girls punished more severely than boys for certain behaviors.
Different risk factors and criminogenic needs.
Belief that girls are less likely to reoffend.
Drivers of delinquency do not differ.
All youths should receive a responsive approach that accounts for trauma, mental health, abuse, neglect, and substance use problems.
Gendered programming may reinforce socially constructed differences.
May increase over representation of males.
Adjust cut points that define risk categories so that fewer female offenders score as high risk.
Separate items and scoring mechanisms.
Incorporate gender as a predictor/scoring item.
Pre-Screen Risk Assessment (PSRA) \(\Rightarrow\) gender specific.
All youths referred to the Juvenile Court.
Level of involvement and intensity of service.
Chipman, George, and McCulloch (2010)
Bayesian perspective and machine learning techniques
Sum-of-trees model equipped with a “regularization prior”
Suited for non-experimental context where decision boundaries and correct predictors and functional form are unknown.
\(p(x) ≡ P [Yi = 1|X] = Φ[G(x)]\)
\(G(x)\equiv\sum_{j=1}^{m}g(x_i;T_j,M_j)\),
\(i\) a particular observation,
\(j\) a particular tree,
\(m\) the number of trees in the model
\(X\) a vector of predictors, and
\(\Phi[.]\) standard normal cumulative distribution function used as the link function.
\(p(T,M,Z|X,Y)\propto p(Y|Z)p(Z|X,T,M)[\Pi_j\Pi_i p(\mu_{ij}|T_j)p(T_j)]\)
Ultimate goal generate a large number of trees given the observed data to estimate \(p(T,M,Z|X,Y)\)
\(\Downarrow\)
Likelihood Function
\(p(Y|Z)p(Z|X,T,M)\)
Regularization Prior
\(p(\mu_{ij}|T_j)\) prior distribution of the terminal node parameters to \(\Uparrow\) probability \(E(Y|x)\) in \(y_{min}\) and \(y_{max}\).
\(p(T_j)\) prior on \(T_j\) tree & includes three considerations: tree size, selection of predictors, and selection of values for splitting rules.
Posterior Sampling
Large parameter space \(\Rightarrow\) intractable calculations \(\Rightarrow\) MCMC algorithm comprised of Gibbs sampler to sample from posterior \(\Rightarrow\) gravitate toward regions of high posterior probability.
\(\Downarrow\)
Bayesian inferences about the estimation of f(x), predictions of y, posterior uncertainty, and the marginal effect of one or more predictors on the response. Model free variable selection.
Youths who received PSRA 1st time July 1, 2008 to June 30, 2014 (n = 15,244).
Girls \(\approx\) 31% (n = 4,682).
Delinquency and social history predictors.
Felony or misdemeanor level adjudication within one year \(\Rightarrow\) recidivism outcome.
|
Girls
|
Boys
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| mean | sd | median | min | max | mean | sd | median | min | max | |
| Age | 2.33 | 1.31 | 3 | 0 | 4 | 2.59 | 1.21 | 3 | 0 | 4 |
| Felonies | 0.26 | 0.75 | 0 | 0 | 6 | 0.54 | 1.08 | 0 | 0 | 6 |
| Mis. | 0.42 | 0.73 | 0 | 0 | 3 | 0.49 | 0.78 | 0 | 0 | 3 |
| PersonFel. | 0.06 | 0.36 | 0 | 0 | 4 | 0.22 | 0.72 | 0 | 0 | 4 |
| Weapons | 0.02 | 0.13 | 0 | 0 | 1 | 0.06 | 0.23 | 0 | 0 | 1 |
| PersonMis. | 0.19 | 0.46 | 0 | 0 | 2 | 0.20 | 0.47 | 0 | 0 | 2 |
| Detention | 0.17 | 0.44 | 0 | 0 | 3 | 0.22 | 0.48 | 0 | 0 | 3 |
| JJS Custody | 0.01 | 0.16 | 0 | 0 | 4 | 0.01 | 0.15 | 0 | 0 | 4 |
| Escapes | 0.00 | 0.08 | 0 | 0 | 2 | 0.00 | 0.05 | 0 | 0 | 1 |
| FTA | 0.02 | 0.16 | 0 | 0 | 2 | 0.01 | 0.13 | 0 | 0 | 2 |
Divide data into training and test groups.
Build a gender neutral and gender specific model using BART.
Estimate predicted probability of reoffending for each observation in the test data using each BART model.
Compare predictive validity indicators.
Explore variable importance for girls vs. boys.
| Girls | Boys | |
|---|---|---|
| Age | √ | √ |
| Felonies | X | X |
| Misdemeanors | √ | √ |
| Person fel. | X | √ |
| Weapons | X | X |
| Person misdemeanors | X | X |
| Detention | √ | √ |
| JJS Custody | X | X |
| Escapes | X | X |
| FTA Warrants | X | X |
| Girls | Boys | |
|---|---|---|
| School | √ | X |
| Friends | √ | √ |
| Child Welfare | X | X |
| Runaway | √ | √ |
| Household | X | X |
| Parent Compliance | √ | √ |
| Substance Use | X | X |
| Abuse/Neglect | X | X |
| Mental Health | X | X |
| Gender Neutral | Gender Specific | |
|---|---|---|
| Girls | ||
| AUC | 0.6797 | 0.6767 |
| Somers’ d | 0.3122 | 0.2909 |
| Boys | ||
| AUC | 0.6948 | 0.6954 |
| Somers’ d | 0.3448 | 0.3311 |
Utah may want to move toward gender neutral assessment.
Some girls may be higher risk and some boys may be lower risk than currently predicted.
No evidence that there is substantial difference between predictors across gender.
Risk factors focus on internal factors, not structural/societal.
Non-experimental context.
Does not address programming questions.
Only included measures of discrimination, not calibration.
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